Overview

Dataset statistics

Number of variables15
Number of observations200
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory50.3 KiB
Average record size in memory257.6 B

Variable types

Text1
Numeric10
Categorical4

Alerts

PatientID has unique valuesUnique
ADAS_Cog has unique valuesUnique
HippocampalVolume has unique valuesUnique
BMI has unique valuesUnique

Reproduction

Analysis started2026-01-18 09:49:01.777041
Analysis finished2026-01-18 09:49:08.637139
Duration6.86 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

PatientID
Text

Unique 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
2026-01-18T20:49:08.784814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters1000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)100.0%

Sample

1st rowP0001
2nd rowP0002
3rd rowP0003
4th rowP0004
5th rowP0005
ValueCountFrequency (%)
p00011
 
0.5%
p00121
 
0.5%
p00241
 
0.5%
p00031
 
0.5%
p00041
 
0.5%
p00051
 
0.5%
p00061
 
0.5%
p00071
 
0.5%
p00081
 
0.5%
p00091
 
0.5%
Other values (190)190
95.0%
2026-01-18T20:49:09.035336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0339
33.9%
P200
20.0%
1140
14.0%
241
 
4.1%
340
 
4.0%
840
 
4.0%
940
 
4.0%
440
 
4.0%
540
 
4.0%
640
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0339
33.9%
P200
20.0%
1140
14.0%
241
 
4.1%
340
 
4.0%
840
 
4.0%
940
 
4.0%
440
 
4.0%
540
 
4.0%
640
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0339
33.9%
P200
20.0%
1140
14.0%
241
 
4.1%
340
 
4.0%
840
 
4.0%
940
 
4.0%
440
 
4.0%
540
 
4.0%
640
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0339
33.9%
P200
20.0%
1140
14.0%
241
 
4.1%
340
 
4.0%
840
 
4.0%
940
 
4.0%
440
 
4.0%
540
 
4.0%
640
 
4.0%

Age
Real number (ℝ)

Distinct35
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.865
Minimum55
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2026-01-18T20:49:09.127425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile56
Q162
median72
Q381
95-th percentile87.05
Maximum89
Range34
Interquartile range (IQR)19

Descriptive statistics

Standard deviation10.590921
Coefficient of variation (CV)0.14737245
Kurtosis-1.2956818
Mean71.865
Median Absolute Deviation (MAD)9.5
Skewness-0.055624582
Sum14373
Variance112.16761
MonotonicityNot monotonic
2026-01-18T20:49:09.198599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
7810
 
5.0%
5610
 
5.0%
869
 
4.5%
829
 
4.5%
698
 
4.0%
558
 
4.0%
878
 
4.0%
808
 
4.0%
637
 
3.5%
897
 
3.5%
Other values (25)116
58.0%
ValueCountFrequency (%)
558
4.0%
5610
5.0%
577
3.5%
585
2.5%
593
 
1.5%
606
3.0%
617
3.5%
627
3.5%
637
3.5%
641
 
0.5%
ValueCountFrequency (%)
897
3.5%
883
 
1.5%
878
4.0%
869
4.5%
852
 
1.0%
844
2.0%
835
2.5%
829
4.5%
815
2.5%
808
4.0%

Gender
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
Female
109 
Male
91 

Length

Max length6
Median length6
Mean length5.09
Min length4

Characters and Unicode

Total characters1018
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Female109
54.5%
Male91
45.5%

Length

2026-01-18T20:49:09.271026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-18T20:49:09.313269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female109
54.5%
male91
45.5%

Most occurring characters

ValueCountFrequency (%)
e309
30.4%
a200
19.6%
l200
19.6%
F109
 
10.7%
m109
 
10.7%
M91
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)1018
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e309
30.4%
a200
19.6%
l200
19.6%
F109
 
10.7%
m109
 
10.7%
M91
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1018
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e309
30.4%
a200
19.6%
l200
19.6%
F109
 
10.7%
m109
 
10.7%
M91
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1018
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e309
30.4%
a200
19.6%
l200
19.6%
F109
 
10.7%
m109
 
10.7%
M91
 
8.9%

EducationYears
Real number (ℝ)

Distinct14
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.405
Minimum6
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2026-01-18T20:49:09.352024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6
Q19
median12
Q316
95-th percentile19
Maximum19
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.1734956
Coefficient of variation (CV)0.33643656
Kurtosis-1.2688881
Mean12.405
Median Absolute Deviation (MAD)3
Skewness0.028229403
Sum2481
Variance17.418065
MonotonicityNot monotonic
2026-01-18T20:49:09.407820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1522
11.0%
620
10.0%
1020
10.0%
1717
8.5%
1816
8.0%
915
7.5%
1215
7.5%
1915
7.5%
815
7.5%
1312
 
6.0%
Other values (4)33
16.5%
ValueCountFrequency (%)
620
10.0%
710
5.0%
815
7.5%
915
7.5%
1020
10.0%
118
 
4.0%
1215
7.5%
1312
6.0%
1410
5.0%
1522
11.0%
ValueCountFrequency (%)
1915
7.5%
1816
8.0%
1717
8.5%
165
 
2.5%
1522
11.0%
1410
5.0%
1312
6.0%
1215
7.5%
118
 
4.0%
1020
10.0%

MMSE_Score
Real number (ℝ)

Distinct184
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.830694
Minimum14.792315
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2026-01-18T20:49:09.475842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14.792315
5-th percentile17.935451
Q120.955254
median23.834134
Q326.500705
95-th percentile30
Maximum30
Range15.207685
Interquartile range (IQR)5.5454505

Descriptive statistics

Standard deviation3.6832471
Coefficient of variation (CV)0.15455895
Kurtosis-0.68662979
Mean23.830694
Median Absolute Deviation (MAD)2.8363182
Skewness-0.021590878
Sum4766.1387
Variance13.566309
MonotonicityNot monotonic
2026-01-18T20:49:09.558787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3017
 
8.5%
26.727811891
 
0.5%
26.016186061
 
0.5%
18.7821221
 
0.5%
26.67869021
 
0.5%
25.466392981
 
0.5%
20.240480851
 
0.5%
21.944532331
 
0.5%
19.763145911
 
0.5%
23.749283611
 
0.5%
Other values (174)174
87.0%
ValueCountFrequency (%)
14.792315341
0.5%
15.50441711
0.5%
15.73023161
0.5%
15.843071291
0.5%
16.965042051
0.5%
17.147461881
0.5%
17.186470241
0.5%
17.574214721
0.5%
17.622289361
0.5%
17.863543321
0.5%
ValueCountFrequency (%)
3017
8.5%
29.919776561
 
0.5%
29.901424871
 
0.5%
29.804574431
 
0.5%
29.474526231
 
0.5%
29.467497071
 
0.5%
29.41548951
 
0.5%
29.110707291
 
0.5%
28.951265251
 
0.5%
28.717760481
 
0.5%

CDR
Categorical

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
1.0
63 
0.5
57 
0.0
43 
2.0
32 
3.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters600
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.063
31.5%
0.557
28.5%
0.043
21.5%
2.032
16.0%
3.05
 
2.5%

Length

2026-01-18T20:49:09.630877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-18T20:49:09.674289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.063
31.5%
0.557
28.5%
0.043
21.5%
2.032
16.0%
3.05
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0243
40.5%
.200
33.3%
163
 
10.5%
557
 
9.5%
232
 
5.3%
35
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0243
40.5%
.200
33.3%
163
 
10.5%
557
 
9.5%
232
 
5.3%
35
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0243
40.5%
.200
33.3%
163
 
10.5%
557
 
9.5%
232
 
5.3%
35
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0243
40.5%
.200
33.3%
163
 
10.5%
557
 
9.5%
232
 
5.3%
35
 
0.8%

ADAS_Cog
Real number (ℝ)

Unique 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.585225
Minimum4.1807059
Maximum51.323821
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2026-01-18T20:49:09.743625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.1807059
5-th percentile10.932489
Q119.153922
median25.272047
Q331.325388
95-th percentile42.555945
Maximum51.323821
Range47.143115
Interquartile range (IQR)12.171466

Descriptive statistics

Standard deviation9.5052668
Coefficient of variation (CV)0.3715139
Kurtosis-0.12945005
Mean25.585225
Median Absolute Deviation (MAD)6.0546507
Skewness0.31754918
Sum5117.0451
Variance90.350097
MonotonicityNot monotonic
2026-01-18T20:49:09.823582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.754797341
 
0.5%
35.654803751
 
0.5%
16.053926981
 
0.5%
23.131283561
 
0.5%
20.602689421
 
0.5%
39.469778841
 
0.5%
26.965547771
 
0.5%
35.318445391
 
0.5%
10.144396271
 
0.5%
27.670502661
 
0.5%
Other values (190)190
95.0%
ValueCountFrequency (%)
4.1807059211
0.5%
6.5912576871
0.5%
7.8983160731
0.5%
7.9741639581
0.5%
8.3903906651
0.5%
9.2977528011
0.5%
9.4337082651
0.5%
9.842558851
0.5%
10.144396271
0.5%
10.915387041
0.5%
ValueCountFrequency (%)
51.323820651
0.5%
50.600845381
0.5%
50.269324261
0.5%
49.55300141
0.5%
47.988981241
0.5%
45.102045391
0.5%
43.124485581
0.5%
42.958776731
0.5%
42.945578641
0.5%
42.708006361
0.5%

APOE4_Status
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
0
107 
1
76 
2
17 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters200
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0107
53.5%
176
38.0%
217
 
8.5%

Length

2026-01-18T20:49:09.897807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-18T20:49:09.943978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0107
53.5%
176
38.0%
217
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0107
53.5%
176
38.0%
217
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0107
53.5%
176
38.0%
217
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0107
53.5%
176
38.0%
217
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0107
53.5%
176
38.0%
217
 
8.5%

CSF_AmyloidBeta42
Real number (ℝ)

Distinct199
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean612.71141
Minimum200
Maximum965.96286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2026-01-18T20:49:10.003486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile363.41666
Q1506.36739
median615.71825
Q3718.69942
95-th percentile853.08152
Maximum965.96286
Range765.96286
Interquartile range (IQR)212.33203

Descriptive statistics

Standard deviation153.92237
Coefficient of variation (CV)0.25121512
Kurtosis-0.13725631
Mean612.71141
Median Absolute Deviation (MAD)109.41812
Skewness-0.11257123
Sum122542.28
Variance23692.096
MonotonicityNot monotonic
2026-01-18T20:49:10.087977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2002
 
1.0%
561.39351941
 
0.5%
684.5863681
 
0.5%
747.2648231
 
0.5%
551.27529251
 
0.5%
225.08914281
 
0.5%
943.64138591
 
0.5%
391.564131
 
0.5%
353.1901881
 
0.5%
753.38556481
 
0.5%
Other values (189)189
94.5%
ValueCountFrequency (%)
2002
1.0%
225.08914281
0.5%
276.9914831
0.5%
300.26989731
0.5%
339.04293321
0.5%
346.05647791
0.5%
349.71238891
0.5%
353.1901881
0.5%
361.050941
0.5%
363.54117651
0.5%
ValueCountFrequency (%)
965.9628611
0.5%
960.51233781
0.5%
943.64138591
0.5%
937.86537081
0.5%
931.57845021
0.5%
921.77236921
0.5%
913.25620571
0.5%
911.51219751
0.5%
887.10470311
0.5%
862.43651151
0.5%

CSF_TotalTau
Real number (ℝ)

Distinct198
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean353.4786
Minimum100
Maximum659.56512
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2026-01-18T20:49:10.170526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile171.38626
Q1269.79968
median350.80392
Q3422.26773
95-th percentile550.73946
Maximum659.56512
Range559.56512
Interquartile range (IQR)152.46805

Descriptive statistics

Standard deviation119.18416
Coefficient of variation (CV)0.33717502
Kurtosis-0.31363993
Mean353.4786
Median Absolute Deviation (MAD)80.089209
Skewness0.14019805
Sum70695.721
Variance14204.863
MonotonicityNot monotonic
2026-01-18T20:49:10.248718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1003
 
1.5%
283.10098581
 
0.5%
296.80612991
 
0.5%
372.00319041
 
0.5%
348.16281811
 
0.5%
419.514981
 
0.5%
364.34964421
 
0.5%
233.23172711
 
0.5%
493.58858021
 
0.5%
330.97645121
 
0.5%
Other values (188)188
94.0%
ValueCountFrequency (%)
1003
1.5%
110.75172291
 
0.5%
112.9440121
 
0.5%
114.03720931
 
0.5%
129.65535521
 
0.5%
133.76234751
 
0.5%
156.37263231
 
0.5%
163.29252251
 
0.5%
171.81224381
 
0.5%
172.53061171
 
0.5%
ValueCountFrequency (%)
659.56512051
0.5%
649.15994211
0.5%
628.31954481
0.5%
609.80028141
0.5%
608.87698561
0.5%
596.74485561
0.5%
591.60646971
0.5%
569.12116561
0.5%
555.40501761
0.5%
551.32409771
0.5%

CSF_PTau
Real number (ℝ)

Distinct197
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.876667
Minimum20
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2026-01-18T20:49:10.328069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile28.704672
Q148.242778
median61.286706
Q375.130526
95-th percentile94.584565
Maximum120
Range100
Interquartile range (IQR)26.887748

Descriptive statistics

Standard deviation20.027852
Coefficient of variation (CV)0.32367373
Kurtosis-0.25456709
Mean61.876667
Median Absolute Deviation (MAD)13.466268
Skewness0.15007196
Sum12375.333
Variance401.11484
MonotonicityNot monotonic
2026-01-18T20:49:10.407244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
204
 
2.0%
66.148133961
 
0.5%
70.318784351
 
0.5%
78.271692521
 
0.5%
43.936420991
 
0.5%
89.853771381
 
0.5%
54.577527981
 
0.5%
59.572654111
 
0.5%
45.055766421
 
0.5%
77.680907931
 
0.5%
Other values (187)187
93.5%
ValueCountFrequency (%)
204
2.0%
22.669267661
 
0.5%
24.631212691
 
0.5%
24.783823761
 
0.5%
24.784744821
 
0.5%
27.697363681
 
0.5%
28.105938651
 
0.5%
28.736183831
 
0.5%
28.749082861
 
0.5%
29.486873711
 
0.5%
ValueCountFrequency (%)
1201
0.5%
112.03366231
0.5%
111.16398571
0.5%
101.23007151
0.5%
101.14990941
0.5%
100.12185781
0.5%
97.691726121
0.5%
97.465955521
0.5%
97.081851331
0.5%
95.445031931
0.5%

HippocampalVolume
Real number (ℝ)

Unique 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3519.9076
Minimum2418.7071
Maximum4535.8255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2026-01-18T20:49:10.492269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2418.7071
5-th percentile2867.4648
Q13278.4288
median3509.1062
Q33769.5596
95-th percentile4146.6421
Maximum4535.8255
Range2117.1184
Interquartile range (IQR)491.13087

Descriptive statistics

Standard deviation379.65002
Coefficient of variation (CV)0.10785795
Kurtosis-0.066309642
Mean3519.9076
Median Absolute Deviation (MAD)255.98991
Skewness-0.058420467
Sum703981.53
Variance144134.14
MonotonicityNot monotonic
2026-01-18T20:49:10.579022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3693.8931271
 
0.5%
3429.6456751
 
0.5%
3881.1498191
 
0.5%
4144.9113031
 
0.5%
4025.9657811
 
0.5%
4155.9858121
 
0.5%
3796.8509961
 
0.5%
3530.1734561
 
0.5%
2859.2136751
 
0.5%
3401.5750051
 
0.5%
Other values (190)190
95.0%
ValueCountFrequency (%)
2418.7070831
0.5%
2630.9321821
0.5%
2639.2738391
0.5%
2651.258041
0.5%
2749.9310121
0.5%
2767.7468411
0.5%
2778.7441291
0.5%
2853.4757571
0.5%
2859.2136751
0.5%
2866.3456231
0.5%
ValueCountFrequency (%)
4535.8254571
0.5%
4368.3770871
0.5%
4325.0099681
0.5%
4275.57161
0.5%
4260.4762741
0.5%
4259.9527741
0.5%
4235.2734711
0.5%
4184.2453491
0.5%
4178.0204161
0.5%
4155.9858121
0.5%

CorticalThickness
Real number (ℝ)

Distinct197
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5996679
Minimum2
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2026-01-18T20:49:10.661328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.2500486
Q12.475537
median2.5986753
Q32.7337346
95-th percentile2.8811664
Maximum3
Range1
Interquartile range (IQR)0.25819757

Descriptive statistics

Standard deviation0.19482659
Coefficient of variation (CV)0.074942876
Kurtosis0.13434617
Mean2.5996679
Median Absolute Deviation (MAD)0.12918174
Skewness-0.34152056
Sum519.93359
Variance0.037957401
MonotonicityNot monotonic
2026-01-18T20:49:10.746233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34
 
2.0%
2.7416428851
 
0.5%
2.5697453431
 
0.5%
2.8112113621
 
0.5%
2.6446477831
 
0.5%
2.58902121
 
0.5%
2.6571108151
 
0.5%
2.7042244861
 
0.5%
2.7290431171
 
0.5%
2.7111208931
 
0.5%
Other values (187)187
93.5%
ValueCountFrequency (%)
21
0.5%
2.0119222731
0.5%
2.0939424951
0.5%
2.1728651571
0.5%
2.1992275271
0.5%
2.2041400381
0.5%
2.2212770671
0.5%
2.2236301911
0.5%
2.2397884531
0.5%
2.2453935061
0.5%
ValueCountFrequency (%)
34
2.0%
2.9916694911
 
0.5%
2.9601022361
 
0.5%
2.9550621791
 
0.5%
2.9465031
 
0.5%
2.9231165651
 
0.5%
2.9197294341
 
0.5%
2.8791367621
 
0.5%
2.878493061
 
0.5%
2.8748876191
 
0.5%

BMI
Real number (ℝ)

Unique 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.978314
Minimum15
Maximum36.03864
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2026-01-18T20:49:10.828584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile18.776092
Q122.6513
median24.910331
Q327.414078
95-th percentile31.202385
Maximum36.03864
Range21.03864
Interquartile range (IQR)4.7627772

Descriptive statistics

Standard deviation3.8078165
Coefficient of variation (CV)0.1524449
Kurtosis0.1029193
Mean24.978314
Median Absolute Deviation (MAD)2.4504042
Skewness0.07489875
Sum4995.6627
Variance14.499466
MonotonicityNot monotonic
2026-01-18T20:49:10.907876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.228490391
 
0.5%
19.969688561
 
0.5%
23.729139611
 
0.5%
30.126575181
 
0.5%
27.230764241
 
0.5%
20.554168171
 
0.5%
25.986019111
 
0.5%
26.992886991
 
0.5%
29.560596151
 
0.5%
31.322162751
 
0.5%
Other values (190)190
95.0%
ValueCountFrequency (%)
151
0.5%
15.244731371
0.5%
15.815276191
0.5%
16.152552351
0.5%
17.533840191
0.5%
17.923696381
0.5%
18.427244761
0.5%
18.569355891
0.5%
18.596384091
0.5%
18.607502591
0.5%
ValueCountFrequency (%)
36.038640161
0.5%
34.650461691
0.5%
33.398887161
0.5%
33.358154891
0.5%
33.097238491
0.5%
33.094424881
0.5%
32.217392441
0.5%
31.645880661
0.5%
31.570691811
0.5%
31.322162751
0.5%

Diagnosis
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
MCI
75 
AD
68 
Control
57 

Length

Max length7
Median length3
Mean length3.8
Min length2

Characters and Unicode

Total characters760
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAD
2nd rowControl
3rd rowMCI
4th rowMCI
5th rowMCI

Common Values

ValueCountFrequency (%)
MCI75
37.5%
AD68
34.0%
Control57
28.5%

Length

2026-01-18T20:49:10.985964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-18T20:49:11.030927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mci75
37.5%
ad68
34.0%
control57
28.5%

Most occurring characters

ValueCountFrequency (%)
C132
17.4%
o114
15.0%
M75
9.9%
I75
9.9%
A68
8.9%
D68
8.9%
n57
7.5%
t57
7.5%
r57
7.5%
l57
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C132
17.4%
o114
15.0%
M75
9.9%
I75
9.9%
A68
8.9%
D68
8.9%
n57
7.5%
t57
7.5%
r57
7.5%
l57
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C132
17.4%
o114
15.0%
M75
9.9%
I75
9.9%
A68
8.9%
D68
8.9%
n57
7.5%
t57
7.5%
r57
7.5%
l57
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C132
17.4%
o114
15.0%
M75
9.9%
I75
9.9%
A68
8.9%
D68
8.9%
n57
7.5%
t57
7.5%
r57
7.5%
l57
7.5%

Interactions

2026-01-18T20:49:07.782738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T20:49:02.169424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-18T20:49:03.326345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T20:49:04.446652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T20:49:05.026825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T20:49:05.720548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T20:49:06.238737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T20:49:06.764675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T20:49:07.269602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T20:49:07.830533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-18T20:49:02.512539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-18T20:49:04.964490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-18T20:49:06.190110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T20:49:06.712655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T20:49:07.220338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-18T20:49:07.734439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-18T20:49:11.079218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ADAS_CogAPOE4_StatusAgeBMICDRCSF_AmyloidBeta42CSF_PTauCSF_TotalTauCorticalThicknessDiagnosisEducationYearsGenderHippocampalVolumeMMSE_Score
ADAS_Cog1.0000.109-0.114-0.1180.106-0.040-0.136-0.038-0.0530.000-0.0410.1470.008-0.004
APOE4_Status0.1091.0000.0000.0670.0190.0000.1860.0000.0000.0000.0000.0000.0000.022
Age-0.1140.0001.000-0.0280.0000.1510.0950.001-0.0850.1640.0000.0000.131-0.020
BMI-0.1180.067-0.0281.0000.057-0.0450.052-0.0190.0020.000-0.0860.0700.043-0.115
CDR0.1060.0190.0000.0571.0000.0000.1360.1240.0210.0000.0470.0000.0000.047
CSF_AmyloidBeta42-0.0400.0000.151-0.0450.0001.0000.0560.039-0.0130.0000.0830.0000.0330.043
CSF_PTau-0.1360.1860.0950.0520.1360.0561.000-0.069-0.0560.1310.0340.073-0.014-0.047
CSF_TotalTau-0.0380.0000.001-0.0190.1240.039-0.0691.0000.0290.0000.0800.148-0.099-0.035
CorticalThickness-0.0530.000-0.0850.0020.021-0.013-0.0560.0291.0000.114-0.0880.0570.0570.054
Diagnosis0.0000.0000.1640.0000.0000.0000.1310.0000.1141.0000.1560.0000.0000.000
EducationYears-0.0410.0000.000-0.0860.0470.0830.0340.080-0.0880.1561.0000.072-0.1400.054
Gender0.1470.0000.0000.0700.0000.0000.0730.1480.0570.0000.0721.0000.1400.061
HippocampalVolume0.0080.0000.1310.0430.0000.033-0.014-0.0990.0570.000-0.1400.1401.000-0.041
MMSE_Score-0.0040.022-0.020-0.1150.0470.043-0.047-0.0350.0540.0000.0540.061-0.0411.000

Missing values

2026-01-18T20:49:08.492222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-18T20:49:08.580685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PatientIDAgeGenderEducationYearsMMSE_ScoreCDRADAS_CogAPOE4_StatusCSF_AmyloidBeta42CSF_TotalTauCSF_PTauHippocampalVolumeCorticalThicknessBMIDiagnosis
0P000183Female1126.7278120.019.7547971561.393519283.10098666.1481343693.8931272.74164328.228490AD
1P000269Female622.7589331.029.8937460349.712389185.62364276.3147443161.4573382.64664321.105815Control
2P000362Female925.2966652.012.7787221659.883468339.40615477.2094703242.5800672.79062726.905431MCI
3P000475Male1523.4794281.032.1299840697.079391659.56512148.3384513911.9843272.65742527.021881MCI
4P000573Male1124.3879841.022.5967460527.522031253.55905256.6575663366.0898672.47751329.240840MCI
5P000677Female1126.3806280.521.2517921836.098014546.69401765.6515993338.5406152.67230136.038640Control
6P000765Male1620.7271171.032.1096001416.135151551.32409855.0261773117.9509682.37125526.569664Control
7P000865Male1730.0000000.029.4426331380.343768283.56941192.1469123669.4396262.62171222.964145AD
8P000978Male1019.9759300.521.3903381633.667773418.27797069.8194994325.0099682.59335424.897703Control
9P001078Male619.1432460.536.5932981757.064745545.40759574.6975563072.9868342.55837717.923696MCI
PatientIDAgeGenderEducationYearsMMSE_ScoreCDRADAS_CogAPOE4_StatusCSF_AmyloidBeta42CSF_TotalTauCSF_PTauHippocampalVolumeCorticalThicknessBMIDiagnosis
190P019179Female1221.6304241.011.2068080421.860237414.59552962.2654083447.4972132.61198723.566639MCI
191P019277Male1420.5440370.517.6907001646.473107478.70088031.2344403530.7407532.65547522.409833AD
192P019385Male824.1940870.024.6687300695.066532306.20567278.3845793410.0576072.87213227.976768Control
193P019484Female1420.6762000.042.9455792662.069865249.29484046.6371183239.9989682.33823624.275103MCI
194P019589Male1925.0818271.019.8238872572.206851224.62289797.4659563567.4618692.00000022.402508Control
195P019661Female623.7990481.027.2378801580.526895114.03720981.6009613676.7762602.63677030.285216AD
196P019770Male623.0442080.024.8357710606.571721596.74485651.0535623063.8403692.96010230.678412Control
197P019880Female1920.3697450.536.8839331577.949700217.61499685.6203274064.3729522.84778922.598306MCI
198P019956Female921.6929150.550.2693240744.581868323.44956561.3571103460.5647472.64193217.533840AD
199P020055Female1427.0215651.019.6913120931.578450316.78240477.0554743507.5398492.50167329.030055AD